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Machine Learning - Neural Networks - Representation

Neural Networks

Model Representation

  1. input and output

    • x1, x2 are input layers which go through a node and then generate an output layer
    • hθ(x) = 1 / (1 + -eθTx)
    • x0 is usually 1, which is also called a bias unit
  2. sigmoid activation function

    • g(z) = 1 / (1 + e-z)
  3. Neural network is a group of neurons put together

    • hidden layers are between the input layer and the output layer
    • weights are also matrix
    • sj units in layer j, sj+1 units in layer j+1
    • the dimension will be sj+1 * (sj + 1)

Intuition

  1. Non-linear classification

    • if data can be clusterd, try to use a simple representation of a given data set
    • x1 XOR x2: true if either one is true
    • x1 XNOR x2 : NOT (x1 XOR x2)
    • x1 AND x2
    • do some calculation and see if the function becomes 1 with x1 and x2 are either 0 or 1
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